Amazon AWS Certified Solutions Architect – Professional SAP-C02 Exam Dumps and Practice Test Questions Set 10 Q181-200

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Question 181:

A company wants to deploy a globally distributed, high-availability relational database with minimal replication lag for their SaaS application. Which solution is most suitable?

Answer:

A) Amazon Aurora Global Database
B) RDS cross-region snapshots
C) EC2-hosted MySQL with manual replication
D) Standby RDS in a single region

Explanation:

The correct answer is A) Amazon Aurora Global Database.

Amazon Aurora Global Database is specifically designed for globally distributed, high-performance relational workloads. It enables the creation of a primary cluster in one region and multiple read-only secondary clusters in other regions. Replication between regions typically experiences less than one second lag, which is ideal for SaaS applications where near real-time global consistency is required.

Aurora Global Database supports automatic failover, allowing secondary clusters to be promoted to primary in case of regional outages. Each cluster is deployed across multiple Availability Zones (AZs) within a region, ensuring resilience against localized infrastructure failures. The design provides low Recovery Point Objective (RPO) and Recovery Time Objective (RTO), critical for disaster recovery planning.

Option B, RDS cross-region snapshots, only provides point-in-time backups, which is unsuitable for real-time global applications due to restoration latency. Option C, EC2-hosted MySQL with manual replication, introduces operational complexity, increases the risk of replication conflicts, and requires continuous monitoring, patching, and scaling. Option D, standby RDS in a single region, cannot provide global availability or protection against regional failures.

Security measures include IAM roles for access control, KMS encryption for data at rest, and TLS encryption in transit. AWS CloudTrail captures administrative actions for auditing. CloudWatch monitors metrics such as replication lag, CPU utilization, storage, and query performance. Alerts can be configured to notify administrators of anomalies, ensuring operational reliability.

Operationally, Aurora Global Database enables read scaling, as read traffic can be offloaded to secondary clusters, reducing load on the primary database. It also automatically scales storage and supports Aurora Serverless for variable workloads, providing cost optimization without manual intervention.

From an SAP-C02 perspective, this architecture demonstrates best practices for multi-region relational databases, emphasizing operational excellence, performance efficiency, reliability, cost optimization, and security. Organizations can use Route 53 latency-based routing to direct users to the closest read replica and integrate CloudFront caching to reduce latency for frequently accessed content. Disaster recovery is simplified, as secondary regions can be promoted to primary with minimal downtime.

Aurora Global Database provides a resilient, low-latency, globally accessible database solution, aligning perfectly with SAP-C02 principles for scalable, highly available, and cost-effective architectures.

Question 182:

A company wants to implement a serverless, real-time analytics pipeline for IoT telemetry data with minimal operational overhead. Which solution is most suitable?

Answer:

A) AWS IoT Core, Lambda, DynamoDB
B) SQS with EC2 consumers
C) SNS with S3 triggers
D) RDS batch processing

Explanation:

The correct answer is A) AWS IoT Core, Lambda, DynamoDB.

AWS IoT Core is a fully managed, highly scalable ingestion service that handles millions of concurrent IoT device connections. Devices can communicate via MQTT, HTTPS, or WebSockets, with authentication managed using X.509 certificates, IAM policies, or custom authorizers. This ensures secure and authorized device communication, which is critical for IoT environments.

Lambda acts as a serverless compute layer, automatically triggered by IoT messages from IoT Core. Lambda functions can filter, transform, enrich, or route data before storage, analytics, or notifications. Being serverless, Lambda scales automatically with incoming data, eliminating manual provisioning or capacity planning.

DynamoDB serves as a low-latency, fully managed NoSQL database for storing processed telemetry data. Multi-AZ replication ensures durability and high availability. DynamoDB Streams enable event-driven workflows, allowing additional processing, alerting, or analytics.

Option B, SQS with EC2 consumers, requires manual instance management, patching, and scaling, increasing operational complexity. Option C, SNS with S3 triggers, is asynchronous and cannot handle high-throughput, low-latency IoT data effectively. Option D, RDS batch processing, introduces latency and requires manual capacity planning, making it unsuitable for real-time processing.

Security measures include IAM roles, KMS encryption, and TLS in transit. CloudTrail logs all administrative actions, while CloudWatch monitors metrics such as ingestion rates, Lambda execution success/failure, and DynamoDB throughput. Alerts allow proactive operational response to anomalies.

Operationally, this architecture scales automatically, maintains low latency, and reduces operational overhead. Pay-per-use pricing of Lambda and DynamoDB ensures cost efficiency. Integration with Timestream, SageMaker, or Kinesis Data Firehose enables predictive analytics, anomaly detection, or advanced analytics in real time.

From an SAP-C02 perspective, this architecture demonstrates best practices for serverless IoT analytics, focusing on operational excellence, reliability, performance efficiency, security, and cost optimization. It provides a fully managed, scalable, and secure pipeline for high-volume IoT telemetry, aligning with modern cloud-native and event-driven design principles.

Question 183:

A company wants to reduce read latency for a globally distributed, high-traffic DynamoDB application while maintaining strong consistency. Which solution is most appropriate?

Answer:

A) DynamoDB Accelerator (DAX)
B) ElastiCache Redis
C) RDS Read Replicas
D) S3 Transfer Acceleration

Explanation:

The correct answer is A) DynamoDB Accelerator (DAX).

DAX is a fully managed, in-memory caching service specifically designed for DynamoDB. It reduces read latency from milliseconds to microseconds while maintaining strong consistency, which is critical for applications such as gaming leaderboards, e-commerce product catalogs, or IoT dashboards where users require instantaneous access to frequently read data.

ElastiCache Redis (option B) can act as a cache but requires additional application logic to maintain consistency with DynamoDB, increasing operational complexity and risk of stale reads. RDS Read Replicas (option C) are applicable only for relational databases, while S3 Transfer Acceleration (option D) optimizes file transfers to S3 rather than database queries.

DAX supports multi-AZ deployment with automatic failover, ensuring high availability. CloudWatch provides metrics on cache hit ratios, latency, and node health. IAM roles enforce access control, and KMS ensures encryption at rest, while TLS protects data in transit.

Operationally, DAX reduces read load on DynamoDB, prevents throttling, and provides predictable, low-latency reads under high traffic. Cost optimization comes from reduced DynamoDB read capacity unit usage, with pay-per-use pricing for DAX nodes.

From an SAP-C02 perspective, DAX demonstrates performance efficiency, operational simplicity, scalability, and reliability. It enables globally distributed applications to maintain low-latency, strongly consistent reads without additional operational complexity. CloudWatch and X-Ray provide end-to-end monitoring, enabling proactive performance troubleshooting.

This architecture aligns with cloud-native design principles, delivering high availability, low latency, operational simplicity, and cost efficiency, ideal for high-traffic DynamoDB applications.

Question 184:

A company wants to orchestrate multiple serverless Lambda functions with conditional logic, retries, and error handling. Which AWS service is most suitable?

Answer:

A) AWS Step Functions
B) Amazon SWF
C) AWS Batch
D) Amazon SQS

Explanation:

The correct answer is A) AWS Step Functions.

Step Functions is a serverless workflow orchestration service that supports sequential or parallel execution of tasks, conditional branching, retries, and error handling. It integrates with Lambda, ECS, SNS, SQS, and other AWS services, enabling scalable, resilient, and event-driven workflows without infrastructure management.

Amazon SWF (option B) is a legacy service requiring manual worker management. AWS Batch (option C) is designed for batch workloads and is unsuitable for real-time event-driven orchestration. Amazon SQS (option D) is a messaging service that cannot orchestrate workflows or implement conditional logic.

Step Functions provides a visual workflow designer, simplifying monitoring, debugging, and execution tracking. CloudWatch monitors execution metrics, and X-Ray provides end-to-end tracing. IAM roles enforce least-privilege access, while KMS secures workflow data.

Operationally, Step Functions reduces manual intervention, ensures reliable execution, and provides automatic retries and error handling. Pay-per-transition billing ensures cost optimization.

From an SAP-C02 perspective, Step Functions demonstrates serverless orchestration best practices, emphasizing operational excellence, reliability, scalability, security, and cost efficiency. Complex workflows execute automatically, supporting modern event-driven, cloud-native architectures.

Question 185:

A company wants a cost-efficient, serverless pipeline to process S3 log files in real time for analytics. Which solution is most suitable?

Answer:

A) S3 event triggers with Lambda and Athena
B) EC2 Auto Scaling with custom scripts
C) RDS batch ingestion
D) SNS to EC2

Explanation:

The correct answer is A) S3 event triggers with Lambda and Athena.

S3 can automatically trigger Lambda functions when new log files are uploaded. Lambda functions process, transform, filter, or enrich data in real time. Athena provides serverless SQL queries directly on S3 objects, enabling analytics without server provisioning.

Option B, EC2 Auto Scaling, increases operational complexity and requires capacity planning. Option C, RDS batch ingestion, introduces latency and requires manual scaling. Option D, SNS to EC2, is asynchronous and unsuitable for high-throughput, real-time analytics.

Security includes IAM roles, KMS encryption, and TLS in transit. CloudTrail logs administrative actions, while CloudWatch monitors Lambda executions, S3 events, and Athena query performance. Alerts enable proactive operational response.

Operationally, this architecture is fully serverless, scalable, durable, and cost-efficient. Lambda and Athena scale automatically based on log volume, and pay-per-use pricing reduces costs. Integration with QuickSight, Glue, EMR, or SageMaker enables advanced analytics or machine learning.

From an SAP-C02 perspective, this design demonstrates serverless analytics best practices, emphasizing operational excellence, reliability, scalability, security, and cost optimization. It provides a resilient, low-maintenance solution for real-time log processing.

Question 186:

A company wants to deploy a globally distributed web application with low latency and automatic failover. Which architecture is most appropriate?

Answer:

A) Multi-region ALBs, CloudFront, and Route 53 latency-based routing
B) Single-region ALB with EC2 Auto Scaling
C) Global Accelerator with single-region EC2
D) S3 static hosting with Transfer Acceleration

Explanation:

The correct answer is A) Multi-region ALBs, CloudFront, and Route 53 latency-based routing.

For a globally distributed application, low latency and high availability are essential. Deploying Application Load Balancers (ALBs) in multiple AWS regions ensures traffic can be directed to the nearest region and provides fault tolerance against regional outages. Each ALB balances traffic across multiple Availability Zones (AZs), preventing localized failures from affecting availability.

CloudFront acts as a Content Delivery Network (CDN), caching static and dynamic content at edge locations worldwide. This reduces latency by serving content closer to the end user. Lambda@Edge allows customization at the edge, such as authentication, A/B testing, or content transformation, further reducing backend load and improving performance.

Route 53 provides latency-based routing, directing user traffic to the nearest healthy region. Health checks monitor endpoints continuously, ensuring traffic is routed only to operational regions. In the event of a regional failure, Route 53 automatically routes users to the next closest region, maintaining application uptime.

Option B, a single-region ALB with EC2 Auto Scaling, has no global failover and presents a single point of failure. Option C, Global Accelerator with a single-region EC2, improves network routing but does not provide regional disaster recovery. Option D, S3 static hosting with Transfer Acceleration, is suitable only for static content and cannot handle dynamic web applications.

Security measures include IAM roles, TLS encryption for secure communication, AWS WAF for application protection, and AWS Shield for DDoS mitigation. CloudTrail logs administrative activities, and CloudWatch monitors ALB metrics, CloudFront cache statistics, and Route 53 health checks. Alerts ensure operational issues are promptly addressed.

Operationally, this architecture scales automatically and reduces administrative overhead. CloudFront caching decreases the load on backend servers, and ALBs automatically handle traffic spikes. Pay-as-you-go pricing ensures cost efficiency.

From an SAP-C02 perspective, this solution demonstrates best practices for globally distributed web applications, emphasizing operational excellence, performance efficiency, reliability, security, and cost optimization. Users worldwide experience fast, reliable access due to intelligent routing, caching, and failover mechanisms.

Question 187:

A company wants to process streaming IoT data in real time without managing servers and visualize analytics dashboards immediately. Which AWS solution is most suitable?

Answer:

A) Kinesis Data Streams, Lambda, DynamoDB, QuickSight
B) SQS with EC2 consumers and RDS
C) SNS with S3 batch triggers
D) RDS batch processing

Explanation:

The correct answer is A) Kinesis Data Streams, Lambda, DynamoDB, QuickSight.

Kinesis Data Streams provides a fully managed, highly scalable ingestion platform capable of handling millions of events per second, ideal for IoT telemetry. Producers (IoT devices) send real-time data to Kinesis, which Lambda functions consume. Lambda scales automatically to handle varying workloads, eliminating the need for server management.

Lambda functions can transform, filter, aggregate, or enrich data before storage. DynamoDB provides low-latency, fully managed NoSQL storage, with multi-AZ replication ensuring high availability and durability. DynamoDB Streams enable additional processing, such as alerts or downstream analytics.

QuickSight provides interactive dashboards for real-time visualization. This enables decision-makers to analyze data without provisioning servers or worrying about scaling.

Option B (SQS with EC2 consumers and RDS) requires manual instance management and scaling. Option C (SNS with S3 triggers) is asynchronous and unsuitable for high-throughput, low-latency analytics. Option D (RDS batch processing) introduces latency and requires capacity planning.

Security is ensured with IAM roles, KMS encryption at rest, and TLS encryption in transit. CloudTrail logs administrative actions, and CloudWatch monitors ingestion rates, Lambda execution metrics, DynamoDB throughput, and QuickSight usage. Alerts enable proactive operational management.

Operationally, this architecture is serverless, scalable, resilient, and cost-efficient. Pay-per-use pricing ensures cost optimization, while Lambda and DynamoDB automatically scale to handle variable workloads. Integration with Timestream or SageMaker allows predictive analytics or anomaly detection.

From an SAP-C02 perspective, this solution demonstrates serverless streaming analytics best practices, emphasizing operational excellence, reliability, performance efficiency, security, and cost optimization. It provides real-time insights, minimal operational overhead, and global scalability, aligning with cloud-native event-driven architecture principles.

Question 188:

A company wants to reduce read latency for a globally distributed, high-traffic DynamoDB application while maintaining strong consistency. Which solution is most appropriate?

Answer:

A) DynamoDB Accelerator (DAX)
B) ElastiCache Redis
C) RDS Read Replicas
D) S3 Transfer Acceleration

Explanation:

The correct answer is A) DynamoDB Accelerator (DAX).

DAX is a fully managed, in-memory caching service for DynamoDB. It reduces read latency from milliseconds to microseconds while maintaining strong consistency, critical for applications such as gaming leaderboards, e-commerce product catalogs, and IoT dashboards where instant access to frequently read data is required.

ElastiCache Redis (option B) requires application-level logic to maintain consistency with DynamoDB. RDS Read Replicas (option C) are relational database solutions and unsuitable for DynamoDB. S3 Transfer Acceleration (option D) optimizes file uploads/downloads but does not improve database read latency.

DAX supports multi-AZ deployment with automatic failover, ensuring high availability. CloudWatch monitors cache hit ratios, latency, and node health. IAM roles enforce access control, KMS encryption protects data at rest, and TLS encrypts data in transit.

Operationally, DAX reduces load on DynamoDB, prevents throttling, and provides predictable, low-latency performance under high traffic. Cost efficiency comes from reducing read capacity units while paying only for DAX nodes consumed.

From an SAP-C02 perspective, DAX demonstrates performance efficiency, operational simplicity, scalability, and reliability. Applications can maintain low-latency, strongly consistent reads without additional operational complexity. CloudWatch and X-Ray monitoring provides full visibility, allowing proactive troubleshooting and performance optimization.

This architecture aligns with cloud-native design principles, ensuring high availability, low latency, operational simplicity, and cost efficiency, making it ideal for mission-critical DynamoDB applications.

Question 189:

A company wants to orchestrate multiple serverless Lambda functions with conditional logic, retries, and error handling. Which AWS service should they use?

Answer:

A) AWS Step Functions
B) Amazon SWF
C) AWS Batch
D) Amazon SQS

Explanation:

The correct answer is A) AWS Step Functions.

Step Functions is a serverless workflow orchestration service that enables sequential or parallel task execution, conditional branching, retries, and error handling. It integrates with Lambda, ECS, SNS, SQS, and other AWS services, enabling complex event-driven workflows without server management.

Amazon SWF (option B) is a legacy orchestration tool that requires manual worker management. AWS Batch (option C) is designed for batch processing and cannot orchestrate serverless event-driven workflows. Amazon SQS (option D) is a messaging service and does not provide workflow orchestration.

Step Functions provides a visual workflow designer, making it easy to monitor, debug, and manage state machines. CloudWatch monitors execution metrics, and X-Ray provides end-to-end tracing. IAM roles enforce least-privilege access, and KMS secures workflow data.

Operationally, Step Functions reduces manual intervention, ensures reliable execution, and provides automatic retries and error handling. Pay-per-transition billing ensures cost efficiency.

From an SAP-C02 perspective, Step Functions demonstrates serverless orchestration best practices, emphasizing operational excellence, reliability, scalability, security, and cost optimization. It allows complex workflows to execute automatically, supporting modern event-driven, cloud-native architectures.

Question 190:

A company wants a cost-efficient, serverless pipeline to process S3 log files in real time for analytics. Which solution is most suitable?

Answer:

A) S3 event triggers with Lambda and Athena
B) EC2 Auto Scaling with custom scripts
C) RDS batch ingestion
D) SNS to EC2

Explanation:

The correct answer is A) S3 event triggers with Lambda and Athena.

S3 can trigger Lambda functions whenever new log files are uploaded. Lambda functions can process, transform, filter, or enrich the log data in real time. Athena enables serverless SQL queries directly on S3 objects, allowing analytics without provisioning servers or databases.

Option B, EC2 Auto Scaling, increases operational complexity and requires capacity management. Option C, RDS batch ingestion, introduces latency and requires manual scaling. Option D, SNS to EC2, is asynchronous and unsuitable for high-throughput, low-latency log analytics.

Security measures include IAM roles, KMS encryption, and TLS in transit. CloudTrail logs all administrative actions, while CloudWatch monitors Lambda executions, S3 events, and Athena query performance. Alerts provide proactive operational management.

Operationally, this architecture is fully serverless, scalable, durable, and cost-efficient. Lambda and Athena automatically scale based on log volume, and pay-per-use pricing optimizes cost. Integration with QuickSight, Glue, EMR, or SageMaker enables advanced analytics or machine learning.

From an SAP-C02 perspective, this design demonstrates serverless analytics best practices, emphasizing operational excellence, reliability, scalability, security, and cost optimization. It provides a resilient, low-maintenance solution suitable for real-time log processing.

Question 191:

A company wants to deploy a multi-region, fault-tolerant relational database for a global SaaS application with minimal replication lag. Which solution is most appropriate?

Answer:

A) Amazon Aurora Global Database
B) RDS cross-region snapshots
C) EC2-hosted MySQL with manual replication
D) Standby RDS in a single region

Explanation:

The correct answer is A) Amazon Aurora Global Database.

Amazon Aurora Global Database is designed for globally distributed, high-performance relational workloads. It allows a primary cluster in one AWS region with read-only secondary clusters in multiple regions. Replication across regions occurs with typically less than one second lag, ensuring near real-time global data access, which is critical for SaaS applications where consistency, availability, and low latency are essential.

Aurora Global Database provides automatic failover capabilities. In the event of a regional outage, a secondary cluster in another region can be promoted to primary, ensuring minimal downtime. Each cluster is also deployed across multiple Availability Zones (AZs) within a region, protecting against localized failures. This architecture delivers low Recovery Point Objective (RPO) and Recovery Time Objective (RTO), which are vital for disaster recovery planning.

Option B, RDS cross-region snapshots, only provides point-in-time backup capabilities. While snapshots are useful for disaster recovery, restoring from them is time-consuming, making them unsuitable for real-time global operations. Option C, EC2-hosted MySQL with manual replication, introduces operational complexity, increases the risk of replication conflicts, and requires ongoing management for scaling, patching, and monitoring. Option D, standby RDS in a single region, cannot provide global availability or protection against regional disasters.

Security measures for Aurora Global Database include IAM roles for access control, KMS encryption for data at rest, and TLS encryption for data in transit. AWS CloudTrail logs all administrative actions, while CloudWatch monitors replication lag, CPU utilization, query performance, and storage metrics. Alerts can be configured for anomalies to allow proactive operational management.

Operationally, Aurora Global Database supports read scaling by offloading read operations to secondary regions, reducing load on the primary cluster and improving global performance. Storage automatically scales to accommodate growing workloads without manual intervention. Aurora Serverless can be implemented for variable workloads, optimizing costs while maintaining high availability and performance.

From an SAP-C02 perspective, Aurora Global Database demonstrates best practices for globally distributed relational databases, emphasizing operational excellence, performance efficiency, reliability, cost optimization, and security. Organizations can use Route 53 latency-based routing to direct users to the closest read replica, and CloudFront caching can further reduce latency for frequently accessed content. Disaster recovery is simplified since secondary regions can be promoted quickly in case of a regional outage.

Aurora Global Database delivers a resilient, low-latency, globally accessible database solution suitable for mission-critical SaaS applications. Its automated features reduce operational overhead while ensuring high availability, global performance, and strong data consistency, aligning perfectly with SAP-C02 principles for cloud-native database architecture.

Question 192:

A company wants a serverless, real-time IoT analytics pipeline with minimal operational overhead. Which AWS solution is most suitable?

Answer:

A) AWS IoT Core, Lambda, DynamoDB
B) SQS with EC2 consumers
C) SNS with S3 triggers
D) RDS batch processing

Explanation:

The correct answer is A) AWS IoT Core, Lambda, DynamoDB.

AWS IoT Core is a fully managed, scalable ingestion platform for IoT devices. It handles millions of simultaneous connections using protocols such as MQTT, HTTPS, or WebSockets. Authentication and authorization are managed through X.509 certificates, IAM policies, or custom authorizers, ensuring secure and reliable device communication.

Lambda acts as a serverless compute layer, automatically triggered by IoT Core messages. Lambda functions can filter, transform, enrich, or route telemetry data for analytics or storage. Being serverless, Lambda scales automatically with workload volume, removing the need for manual provisioning, scaling, or patching.

DynamoDB serves as a low-latency, fully managed NoSQL database for storing processed telemetry data. Multi-AZ replication ensures high availability and durability, while DynamoDB Streams support downstream workflows, such as real-time alerting or triggering additional analytics processes.

Option B, SQS with EC2 consumers, introduces operational complexity, requiring manual scaling, patching, and monitoring. Option C, SNS with S3 triggers, is asynchronous and not optimized for high-throughput, low-latency IoT data streams. Option D, RDS batch processing, introduces delays and requires manual capacity management, making it unsuitable for real-time analytics.

Security is enforced through IAM roles, KMS encryption, and TLS in transit. CloudTrail logs administrative actions, while CloudWatch monitors ingestion rates, Lambda executions, and DynamoDB performance. Alerts enable proactive operational response to anomalies.

Operationally, this architecture scales automatically, provides low-latency processing, and minimizes operational overhead. Pay-per-use pricing ensures cost efficiency. Integration with Timestream or SageMaker allows advanced analytics, predictive modeling, and anomaly detection.

From an SAP-C02 perspective, this solution demonstrates serverless IoT analytics best practices, emphasizing operational excellence, reliability, performance efficiency, security, and cost optimization. It provides a fully managed, resilient, and globally scalable pipeline for high-volume telemetry data, aligning with modern cloud-native, event-driven design principles.

Question 193:

A company wants to reduce read latency for a globally distributed, high-traffic DynamoDB application while maintaining strong consistency. Which solution is most appropriate?

Answer:

A) DynamoDB Accelerator (DAX)
B) ElastiCache Redis
C) RDS Read Replicas
D) S3 Transfer Acceleration

Explanation:

The correct answer is A) DynamoDB Accelerator (DAX).

DAX is a fully managed, in-memory caching service for DynamoDB. It reduces read latency from milliseconds to microseconds while maintaining strong consistency, which is critical for applications such as gaming leaderboards, e-commerce catalogs, and IoT dashboards.

ElastiCache Redis (option B) requires application-level logic to maintain consistency with DynamoDB, increasing operational complexity. RDS Read Replicas (option C) are applicable only for relational databases and are incompatible with DynamoDB. S3 Transfer Acceleration (option D) optimizes object uploads/downloads but does not improve database read performance.

DAX supports multi-AZ deployment with automatic failover, ensuring high availability. CloudWatch monitors cache hit ratios, latency, and node health. IAM roles enforce access control, while KMS ensures encryption at rest, and TLS secures data in transit.

Operationally, DAX reduces load on DynamoDB, prevents throttling, and provides predictable, low-latency performance under high traffic. Cost efficiency is achieved by reducing read capacity unit usage while paying only for DAX nodes.

From an SAP-C02 perspective, DAX demonstrates performance efficiency, operational simplicity, scalability, and reliability. Applications can maintain low-latency, strongly consistent reads without additional operational complexity. CloudWatch and X-Ray monitoring provide end-to-end visibility for proactive performance management.

This architecture aligns with cloud-native principles, delivering high availability, low latency, operational simplicity, and cost efficiency, making it ideal for mission-critical DynamoDB workloads.

Question 194:

A company wants to orchestrate multiple serverless Lambda functions with conditional logic, retries, and error handling. Which AWS service is most suitable?

Answer:

A) AWS Step Functions
B) Amazon SWF
C) AWS Batch
D) Amazon SQS

Explanation:

The correct answer is A) AWS Step Functions.

Step Functions is a serverless orchestration service that supports sequential and parallel execution, conditional branching, retries, and error handling. It integrates with Lambda, ECS, SNS, and SQS, enabling complex event-driven workflows without server management.

Amazon SWF (option B) is a legacy orchestration tool requiring manual worker management. AWS Batch (option C) is for batch workloads and cannot orchestrate serverless event-driven tasks. Amazon SQS (option D) is a messaging service and cannot manage workflow orchestration.

Step Functions provides a visual workflow designer, simplifying monitoring and debugging. CloudWatch monitors execution metrics, and X-Ray provides end-to-end tracing. IAM roles enforce least-privilege access, and KMS secures workflow data.

Operationally, Step Functions reduces manual intervention, ensures reliable execution, and provides automatic retries and error handling. Pay-per-transition billing optimizes cost.

From an SAP-C02 perspective, Step Functions demonstrates serverless orchestration best practices, emphasizing operational excellence, reliability, scalability, security, and cost efficiency. It supports modern cloud-native, event-driven workflows, reducing operational overhead while maintaining reliability and flexibility.

Question 195:

A company wants a cost-efficient, serverless pipeline to process S3 log files in real time for analytics. Which solution is most suitable?

Answer:

A) S3 event triggers with Lambda and Athena
B) EC2 Auto Scaling with custom scripts
C) RDS batch ingestion
D) SNS to EC2

Explanation:

The correct answer is A) S3 event triggers with Lambda and Athena.

S3 can trigger Lambda functions when new log files are uploaded. Lambda functions can process, transform, filter, or enrich data in real time. Athena allows serverless SQL queries directly on S3 objects, enabling analytics without provisioning servers or databases.

Option B, EC2 Auto Scaling, increases operational complexity and requires capacity planning. Option C, RDS batch ingestion, introduces latency and requires manual scaling. Option D, SNS to EC2, is asynchronous and unsuitable for high-throughput, real-time analytics.

Security includes IAM roles, KMS encryption, and TLS in transit. CloudTrail logs administrative actions, while CloudWatch monitors Lambda executions, S3 events, and Athena query performance. Alerts enable proactive operational management.

Operationally, this architecture is serverless, scalable, durable, and cost-efficient. Lambda and Athena automatically scale with log volume, and pay-per-use pricing optimizes cost. Integration with QuickSight, Glue, EMR, or SageMaker enables advanced analytics and machine learning.

From an SAP-C02 perspective, this architecture demonstrates serverless analytics best practices, emphasizing operational excellence, reliability, scalability, security, and cost optimization. It provides a resilient, low-maintenance solution suitable for real-time log processing and analysis.

Question 196:

A company wants to deploy a globally distributed web application with low latency, high availability, and automatic failover. Which architecture is most appropriate?

Answer:

A) Multi-region ALBs, CloudFront, and Route 53 latency-based routing
B) Single-region ALB with EC2 Auto Scaling
C) Global Accelerator with single-region EC2
D) S3 static hosting with Transfer Acceleration

Explanation:

The correct answer is A) Multi-region ALBs, CloudFront, and Route 53 latency-based routing.

For globally distributed applications, low latency, fault tolerance, and high availability are critical. Deploying Application Load Balancers (ALBs) in multiple AWS regions ensures traffic can be routed to the closest healthy region, protecting against regional outages. Each ALB distributes traffic across multiple Availability Zones (AZs), preventing localized failures from affecting application availability.

CloudFront acts as a Content Delivery Network (CDN) that caches static and dynamic content at edge locations worldwide, reducing latency for end users. Lambda@Edge can be integrated to perform real-time content customization at the edge, such as A/B testing, content transformation, or authentication, further decreasing the load on backend servers and improving performance.

Route 53 provides latency-based routing, ensuring users are directed to the nearest healthy region. Health checks continuously monitor endpoints, automatically diverting traffic away from unhealthy regions. In the event of a regional outage, Route 53 ensures automatic failover to the next closest region, maintaining high availability.

Option B, a single-region ALB with EC2 Auto Scaling, lacks global failover and presents a single point of failure. Option C, Global Accelerator with single-region EC2, improves network routing but does not provide multi-region redundancy. Option D, S3 static hosting with Transfer Acceleration, is suitable only for static content and cannot handle dynamic web applications with high availability requirements.

Security measures include IAM roles, TLS encryption, AWS WAF for application-level protection, and AWS Shield for DDoS mitigation. CloudTrail logs administrative activity, while CloudWatch monitors ALB metrics, CloudFront cache hit/miss rates, and Route 53 health check status. Alerts allow proactive response to operational issues.

Operationally, this architecture scales automatically with traffic and reduces operational overhead. CloudFront caching minimizes backend server load, and ALBs automatically handle traffic spikes. Pay-as-you-go pricing ensures cost efficiency.

From an SAP-C02 perspective, this solution demonstrates best practices for globally distributed applications, emphasizing operational excellence, performance efficiency, reliability, security, and cost optimization. Users worldwide experience low-latency, highly available access due to intelligent routing, caching, and failover mechanisms.

Question 197:

A company wants to implement a serverless, real-time analytics pipeline for IoT telemetry data with minimal operational overhead. Which AWS solution is most suitable?

Answer:

A) AWS IoT Core, Lambda, DynamoDB
B) SQS with EC2 consumers
C) SNS with S3 batch triggers
D) RDS batch processing

Explanation:

The correct answer is A) AWS IoT Core, Lambda, DynamoDB.

AWS IoT Core is a fully managed ingestion platform capable of handling millions of simultaneous device connections. Devices can communicate via MQTT, HTTPS, or WebSockets, with authentication handled using X.509 certificates, IAM policies, or custom authorizers. This ensures secure, reliable communication for IoT devices.

Lambda functions act as a serverless compute layer, automatically triggered by IoT Core messages. Lambda can filter, enrich, transform, and route telemetry data for storage or analytics, scaling automatically with incoming traffic. Being serverless, there is no need to provision servers or manage infrastructure, reducing operational complexity.

DynamoDB provides low-latency, scalable NoSQL storage for processed telemetry data. Multi-AZ replication ensures high availability and durability, while DynamoDB Streams enable downstream workflows, such as real-time alerting or additional analytics.

Option B (SQS with EC2 consumers) requires manual instance management, scaling, and patching, increasing operational complexity. Option C (SNS with S3 triggers) is asynchronous and cannot handle high-throughput, low-latency IoT data efficiently. Option D (RDS batch processing) introduces latency and requires capacity management, making it unsuitable for real-time analytics.

Security measures include IAM roles, KMS encryption at rest, and TLS in transit. CloudTrail logs administrative actions, and CloudWatch monitors ingestion rates, Lambda execution metrics, and DynamoDB throughput. Alerts provide proactive operational management.

Operationally, this architecture scales automatically, maintains low-latency processing, and minimizes operational overhead. Lambda and DynamoDB automatically scale to handle variable workloads, while pay-per-use pricing ensures cost efficiency. Integration with Timestream, SageMaker, or QuickSight allows for predictive analytics, anomaly detection, and interactive dashboards in real time.

From an SAP-C02 perspective, this solution demonstrates serverless IoT analytics best practices, emphasizing operational excellence, reliability, performance efficiency, security, and cost optimization. It delivers a fully managed, resilient, and globally scalable pipeline for high-volume telemetry data, aligned with event-driven cloud-native architecture principles.

Question 198:

A company wants to reduce read latency for a globally distributed, high-traffic DynamoDB application while maintaining strong consistency. Which solution is most appropriate?

Answer:

A) DynamoDB Accelerator (DAX)
B) ElastiCache Redis
C) RDS Read Replicas
D) S3 Transfer Acceleration

Explanation:

The correct answer is A) DynamoDB Accelerator (DAX).

DAX is a fully managed, in-memory caching service for DynamoDB. It reduces read latency from milliseconds to microseconds while maintaining strong consistency, which is critical for real-time applications such as gaming leaderboards, e-commerce catalogs, and IoT dashboards.

ElastiCache Redis (option B) requires application-level logic to maintain cache consistency with DynamoDB, increasing complexity and the risk of stale reads. RDS Read Replicas (option C) apply only to relational databases, and S3 Transfer Acceleration (option D) optimizes file uploads/downloads but does not improve database query performance.

DAX supports multi-AZ deployment with automatic failover, ensuring high availability. CloudWatch monitors cache hit ratios, latency, and node health. IAM roles enforce access control, and KMS encryption protects data at rest, while TLS secures data in transit.

Operationally, DAX reduces DynamoDB load, prevents throttling, and provides predictable, low-latency reads under high traffic. Cost optimization is achieved by reducing read capacity unit usage while paying only for DAX nodes.

From an SAP-C02 perspective, DAX demonstrates performance efficiency, operational simplicity, scalability, and reliability. It allows globally distributed applications to maintain low-latency, strongly consistent reads without adding operational overhead. CloudWatch and X-Ray provide monitoring and troubleshooting capabilities, aligning with cloud-native design principles.

This architecture ensures high availability, low latency, operational simplicity, and cost efficiency, making it ideal for high-traffic DynamoDB workloads.

Question 199:

A company wants to orchestrate multiple serverless Lambda functions with conditional logic, retries, and error handling. Which AWS service is most suitable?

Answer:

A) AWS Step Functions
B) Amazon SWF
C) AWS Batch
D) Amazon SQS

Explanation:

The correct answer is A) AWS Step Functions.

Step Functions is a serverless orchestration service that supports sequential and parallel execution, conditional branching, retries, and error handling. It integrates with Lambda, ECS, SNS, SQS, and other AWS services, enabling complex event-driven workflows without server management.

Amazon SWF (option B) is a legacy service requiring manual worker management. AWS Batch (option C) is designed for batch workloads, not real-time orchestration. Amazon SQS (option D) is a messaging service and cannot manage workflows or conditional execution.

Step Functions provides a visual workflow designer, simplifying monitoring, debugging, and operational management. CloudWatch monitors execution metrics, while X-Ray provides end-to-end tracing. IAM roles enforce least-privilege access, and KMS secures workflow data.

Operationally, Step Functions reduces manual intervention, ensures reliable execution, and provides automatic retries and error handling. Pay-per-transition billing ensures cost efficiency, and workflows can be updated or extended without downtime.

From an SAP-C02 perspective, Step Functions demonstrates serverless orchestration best practices, emphasizing operational excellence, reliability, scalability, security, and cost optimization. It supports modern cloud-native, event-driven workflows, enabling robust automation for complex applications.

Question 200:

A company wants a cost-efficient, serverless pipeline to process S3 log files in real time for analytics. Which solution is most suitable?

Answer:

A) S3 event triggers with Lambda and Athena
B) EC2 Auto Scaling with custom scripts
C) RDS batch ingestion
D) SNS to EC2

Explanation:

The correct answer is A) S3 event triggers with Lambda and Athena.

S3 can trigger Lambda functions whenever new log files are uploaded. Lambda functions can process, transform, filter, or enrich the log data in real time. Athena provides serverless SQL query capabilities directly on S3 objects, enabling analytics without provisioning servers or databases.

Option B, EC2 Auto Scaling, introduces operational complexity and requires manual capacity planning. Option C, RDS batch ingestion, is batch-oriented and cannot provide real-time analytics. Option D, SNS to EC2, is asynchronous and unsuitable for high-throughput log processing.

Security measures include IAM roles, KMS encryption, and TLS encryption in transit. CloudTrail logs administrative activity, while CloudWatch monitors Lambda executions, S3 event triggers, and Athena query performance. Alerts allow proactive operational response to anomalies.

Operationally, this architecture is fully serverless, scalable, durable, and cost-efficient. Lambda and Athena scale automatically with log volume, and pay-per-use pricing ensures cost optimization. Integration with QuickSight, Glue, EMR, or SageMaker enables advanced analytics, machine learning, and interactive dashboards.

From an SAP-C02 perspective, this design demonstrates serverless analytics best practices, emphasizing operational excellence, reliability, scalability, security, and cost efficiency. It provides a resilient, low-maintenance solution suitable for real-time log processing, aligned with cloud-native, event-driven design principles.